A hybrid model integrating recurrent neural networks and the semi-supervised support vector machine for identification of early student dropout risk by Huong Nguyen Thi Cam, Aliza Sarlan, Noreen Izza Arshad

“…This research develops an efficient prediction model using machine learning (ML) and deep learning (DL) techniques for identifying student dropouts in both small and big educational datasets. …”
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Emerging trends in the evolution of neuropsychology and artificial intelligence: A comprehensive analysis by Haihua Ying, Andri Pranolo, Zalik Nuryana, Andini Isti Syafitri

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A Robust Framework for Bamboo Forest AGB Estimation by Integrating Geostatistical Prediction and Ensemble Learning by Lianjin Fu, Qingtai Shu, Cuifen Xia, Zeyu Li, Hailing He, Zhengying Li, Shaoyang Ma, Chaoguan Qin, Rong Wei, Qin Xiang, Xiao Zhang, Yiran Zhang, Huashi Cai

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Using Convolutional Neural Networks for segmentation of brain tumors by Kauã Gabriel Silva de Lima, Vagner Alves Ferreira da Silva, João Victor Oliveira da Silva, Lucas Patrick Ramos de Oliveira, Diogo Lopes da Silva

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Enhanced Prediction of Bond Strength in Corroded RC Structures Using Advanced Feature Selection and Ensemble Learning Framework by Jin-Yang Gui, Zhao-Hui Lu, Chun-Qing Li

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Use of self-organizing maps for the classification of cardiometabolic risk and physical fitness in adolescents by Rodrigo Yáñez-Sepúlveda, Rodrigo Olivares, Camilo Ravelo, Guillermo Cortés-Roco, Juan Pablo Zavala-Crichton, Claudio Hinojosa-Torres, Josivaldo de Souza-Lima, Matías Monsalves-Álvarez, Tomás Reyes-Amigo, Juan Hurtado-Almonacid, Jacqueline Páez-Herrera, Sandra Mahecha-Matsudo, Jorge Olivares-Arancibia, Vicente Javier Clemente-Suárez

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A review on NLP zero-shot and few-shot learning: methods and applications by G. Ramesh, Mahammad Sahil, Shashank A. Palan, Darshan Bhandary, Teli Abhishek Ashok, J. Shreyas, N. Sowjanya

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Interpretable Machine Learning Predictions of Bruch’s Membrane Opening-Minimum Rim Width Using Retinal Nerve Fiber Layer Values and Visual Field Global Indexes by Sat Byul Seo, Hyun-kyung Cho

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Prediction of Lithium-Ion Battery State of Health Using a Deep Hybrid Kernel Extreme Learning Machine Optimized by the Improved Black-Winged Kite Algorithm by Juncheng Fu, Zhengxiang Song, Jinhao Meng, Chunling Wu

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Forecasting Hospitalization for Adult Asthma Patients in Emergency Departments Based on Multiple Environmental and Clinical Factors by Xi H, Zhang Y, Li W, Zhang C, Sun Y, Ji H, He Z, Chang C

“…The most critical parameters for predicting hospitalization were found to be illness severity, oxygen saturation, age, and heart rate.Interpretation: Machine learning (ML) models based on clinical, meteorological, and air pollution data can rapidly and accurately predict hospitalization of adult asthma patients in EDs.Keywords: asthma exacerbation, machine learning, emergency department…”
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Exploration of heterogeneity of treatment effects across exercise-based interventions for knee osteoarthritis by Paul A. Dennis, Livia Anderson, Cynthia J. Coffman, Sara Webb, Kelli D. Allen

“…Three metalearners with three machine learning algorithms each and a simple interpretable model-based regression tree were used to identify subgroups with differential treatment effects. …”
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Development and evaluation of a machine vision and deep learning-based smart sprayer system for site-specific weed management in row crops: An edge computing approach by Arjun Upadhyay, Sunil G C, Yu Zhang, Cengiz Koparan, Xin Sun

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Deep Learning-Enhanced Motor Training: A Hybrid VR and Exoskeleton System for Cognitive–Motor Rehabilitation by Kathya P. Acuña Luna, Edgar Rafael Hernandez-Rios, Victor Valencia, Carlos Trenado, Christian Peñaloza

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Developing and validating an artificial intelligence-based application for predicting some pregnancy outcomes: a multi-phase study protocol by Fatemeh Shabani, Ata Jodeiri, Sakineh Mohammad‑Alizadeh‑Charandabi, Fatemeh Abbasalizadeh, Jafar Tanha, Mojgan Mirghafourvand

“…In Phase 2, an artificial intelligence model will be developed using machine learning algorithms such as Random Forest, XGBoost, Support Vector Machines (SVM), and neural networks, followed by model training, validation, and integration into a user-friendly application. …”
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Predicting and Preventing School Dropout with Business Intelligence: Insights from a Systematic Review by Diana-Margarita Córdova-Esparza, Juan Terven, Julio-Alejandro Romero-González, Karen-Edith Córdova-Esparza, Rocio-Edith López-Martínez, Teresa García-Ramírez, Ricardo Chaparro-Sánchez

“…To address this complex issue, educational institutions increasingly rely on business intelligence (BI) and related predictive analytics, such as machine learning and data mining techniques. …”
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Analyzing customer churn behavior using datamining approach: hybrid support vector machine and logistic regression in retail chain by Mohammad Barzegar, Aliakbar Hasani

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Justification of the Architecture a Promising Automated System for Monitoring Radiation, Chemical and Biological Environment Using Artificial Intelligence by S. A. Sharov, D. S. Batinov, M. A. Osipov, M. V. Domnin, S. A. Morozov, M. A. Golyshev, Yu. I. Khripkov, A. V. Nadein, I. V. Chebykin, V. D. Vasin, M. M. Bets

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Using Encyclopedic Texts for Training and Inference of Artificial Neural Networks by Vladimir Medeyko

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Evaluating AI Methods for Pulse Oximetry: Performance, Clinical Accuracy, and Comprehensive Bias Analysis by Ana María Cabanas, Nicolás Sáez, Patricio O. Collao-Caiconte, Pilar Martín-Escudero, Josué Pagán, Elena Jiménez-Herranz, José L. Ayala

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Unveiling shadows: A data-driven insight on depression among Bangladeshi university students by Sanjib Kumar Sen, Md. Shifatul Ahsan Apurba, Anika Priodorshinee Mrittika, Md. Tawhid Anwar, A.B.M. Alim Al Islam, Jannatun Noor

“…To achieve these objectives, a survey was meticulously designed in collaboration with psychologists, counselors, and therapists. Seven machine learning models, including Support Virtual Machine (SVM), K-Nearest Neighbor (K-NN), Gaussian Naive Bayes (GNB), Decision Tree (DT), Random Forest Classifier (RFC), Artificial Neural Network (ANN), and Gradient Boosting (GB), were trained and tested using the collected data (n = 750) to identify the most effective method for predicting depression. …”
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